Genome wide selection: experiences from the Australian Dairy Industry.
B.Tier(1,2), J. Cavanagh(1,3), R.E. Crump(1,2), M. Khatkar(1,3), G. Moser(1), J.
Sölkner(1,3), P.C. Thomson(1,3), A. Woolaston(1,2), K. Zenger(1,3) and H.W.
Raadsma(1,3).
1 Cooperative Research Centre for Innovative Dairy Products, 2 Animal Genetics
and Breeding Unit*, University of New England, Armidale NSW 2351,
Australia, 3 Centre for Advanced
Technologies in Animal Genetics and
reproduction(Reprogen), University of Sydney, Camden NSW 2570.
Semen from almost 1900 progeny tested bulls born between 1950 and 2003 and used
in the Australian dairy industry was available as well as 300 young bulls born in 2006
as candidates for genome wide selection (GWS). Initially 1546 of these sires, chosen
from across the temporal and performance spectra, were genotyped for 15,380 SNP
that spanned the whole bovine genome. The genotypic results were validated using
information provided by ParAllele (now Affymetrix), by open and blind replication of
some samples and by comparing the genotypes of sons with those of their sires for
excessive occurrences of opposing homozygotes. After editing to delete poor quality
SNP and those where the minimum allele frequency was less than 0.01 only 10,715
SNP remained.
Highly accurate estimated breeding values (EBVs) for a number of production,
fertility and fitness traits, provided by the Australian Dairy Herd Improvement
Scheme, were used with this genotypic information to examine some alternative
methods for predicting the genetic merit of animals without (EBV) information. These
methods were broadly based on regression of phenotypes (EBVs) on a subset of the
1546 genotyped sires (the ‘training’ set) - to predict the genetic merit of
complementary group of sires (the ‘test ’ set), without direct use of the pedigree. With
10,715 variables there are a myriad of regression models that could be used to explain
1546 data points. Considerable use was made of internal cross-validation - where
the training set is further divided into a number of other subsets - to find the best
models. Of these methods, two - Partial Least Squares (PLS) and regression using a
genetic algorithm to find optimal solutions (GAR) - provided moderate to high
predictive power (correlations of the order of 0.75 between predictions and EBVs of
the ‘test’ subset of individuals without use of pedigree), with PLS more
computationally efficient than GAR.
These methods were used to identify a subset of important SNP that were
subsequently used to genotype 300 young bulls. The genetic merit of these young
bulls was used as a basis to select among them for entry into a progeny testing
program. An additional 500 sires - made up of two young sire teams, international
sires of interest and some of the younger, untested sires from the 1900 original bulls
are now being tested to further examine the efficacy of these, and other models for
selection of sires and identifying the most useful SNP. Finally, some implications for
industry of this new technology are explored.
*AGBU is a joint venture of NSW Department of Primary Industries and the
University of New England.